{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T10:50:42Z","timestamp":1769338242449,"version":"3.49.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFB2104003"],"award-info":[{"award-number":["2020YFB2104003"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In the field of preference-based evolutionary multiobjective optimization, optimization algorithms are required to search for the Pareto optimal solutions preferred by the decision maker (DM). The reference point is a type of techniques that effectively describe the preferences of DM. So far, the reference point is either static or interactive with the evolutionary process. However, the existing reference point techniques do not cover all application scenarios. A novel case, i.e., the reference point changes over time due to the environment change, has not been considered. This paper focuses on the multiobjective optimization problems with dynamic preferences of the DM. First, we propose a change model of the reference point to simulate the change of the preference by the DM over time. Then, a dynamic preference-based multiobjective evolutionary algorithm framework with a clonal selection algorithm (\u011da-NSCSA) and a genetic algorithm (\u011da-NSGA-II) is designed to solve such kind of optimization problems. In addition, in terms of practical applications, the experiments on the portfolio optimization problems with the dynamic reference point model are tested. Experimental results on the benchmark problems and the practical applications show that \u011da-NSCSA exhibits better performance among the compared optimization algorithms.<\/jats:p>","DOI":"10.1007\/s40747-022-00860-0","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T02:02:35Z","timestamp":1662948155000},"page":"1415-1437","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A novel dynamic reference point model for preference-based evolutionary multiobjective optimization"],"prefix":"10.1007","volume":"9","author":[{"given":"Xin","family":"Lin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8357-1655","authenticated-orcid":false,"given":"Wenjian","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Naijie","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Qingfu","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"key":"860_CR1","doi-asserted-by":"crossref","unstructured":"Dutta S, Das KN (2019) A survey on Pareto-based EAs to solve multi-objective optimization problems. Soft Comput Prob Solv:807\u2013820","DOI":"10.1007\/978-981-13-1595-4_64"},{"issue":"10","key":"860_CR2","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1140\/epjs\/s11734-021-00206-w","volume":"230","author":"N Saini","year":"2021","unstructured":"Saini N, Saha S (2021) Multi-objective optimization techniques: a survey of the state-of-the-art and applications. Eur Phys J Spec Top 230(10):2319\u20132335","journal-title":"Eur Phys J Spec Top"},{"key":"860_CR3","doi-asserted-by":"publisher","unstructured":"Liu S, Zhan Z, Tan KC, Zhang J (2021) A multiobjective framework for many-objective optimization, IEEE Trans Cybern:1\u201315. https:\/\/doi.org\/10.1109\/TCYB.2021.3082200","DOI":"10.1109\/TCYB.2021.3082200"},{"issue":"2","key":"860_CR4","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/TEVC.2018.2791283","volume":"23","author":"Y Sun","year":"2018","unstructured":"Sun Y, Yen GG, Yi Z (2018) IGD indicator-based evolutionary algorithm for many-objective optimization problems. IEEE Trans Evol Comput 23(2):173\u2013187","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"860_CR5","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.1109\/TEVC.2020.2987559","volume":"24","author":"K Li","year":"2020","unstructured":"Li K, Liao M, Deb K, Min G, Yao X (2020) Does preference always help? A holistic study on preference-based evolutionary multiobjective optimization using reference points. IEEE Trans Evolut Comput 24(6):1078\u20131096","journal-title":"IEEE Trans Evolut Comput"},{"issue":"2","key":"860_CR6","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182\u2013197","journal-title":"IEEE Trans Evolut Comput"},{"issue":"6","key":"860_CR7","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q Zhang","year":"2007","unstructured":"Zhang Q, Li H (2007) MOEA\/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolut Comput 11(6):712\u2013731","journal-title":"IEEE Trans Evolut Comput"},{"key":"860_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2021.102141","volume":"71","author":"M Yuan","year":"2021","unstructured":"Yuan M, Li Y, Zhang L, Pei F (2021) Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm. Robot Comput Integrat Manufact 71:102141","journal-title":"Robot Comput Integrat Manufact"},{"key":"860_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2020.05.082","volume":"539","author":"W Wang","year":"2020","unstructured":"Wang W, Li K, Tao X, Gu F (2020) An improved MOEA\/D algorithm with an adaptive evolutionary strategy. Inform Sci 539:1\u201315","journal-title":"Inform Sci"},{"issue":"3","key":"860_CR10","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1002\/mar.20268","volume":"26","author":"E Reutskaja","year":"2009","unstructured":"Reutskaja E, Hogarth RM (2009) Satisfaction in choice as a function of the number of alternatives: when \u201cgoods satiate\u2019\u2019. Psychol Market 26(3):197\u2013203","journal-title":"Psychol Market"},{"key":"860_CR11","doi-asserted-by":"crossref","unstructured":"Bollen D, Knijnenburg BP, Willemsen MC, Graus M (2010) Understanding choice overload in recommender systems. In: Proceedings of the ACM Conference on Recommender Systems, pp. 63\u201370","DOI":"10.1145\/1864708.1864724"},{"key":"860_CR12","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.artint.2015.06.007","volume":"228","author":"M Li","year":"2015","unstructured":"Li M, Yang S, Liu X (2015) Bi-goal evolution for many-objective optimization problems. Artif Intell 228:45\u201365","journal-title":"Artif Intell"},{"issue":"4","key":"860_CR13","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s40747-017-0053-9","volume":"3","author":"H Wang","year":"2017","unstructured":"Wang H, Olhofer M, Jin Y (2017) A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges. Complex Intell Syst 3(4):233\u2013245","journal-title":"Complex Intell Syst"},{"issue":"4","key":"860_CR14","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","volume":"18","author":"K Deb","year":"2013","unstructured":"Deb K, Jain H (2013) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans Evolu Comput 18(4):577\u2013601","journal-title":"IEEE Trans Evolu Comput"},{"issue":"5","key":"860_CR15","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1109\/TEVC.2010.2041060","volume":"14","author":"LB Said","year":"2010","unstructured":"Said LB, Bechikh S, Gh\u00e9dira K (2010) The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making. IEEE Trans Evol Comput 14(5):801\u2013818","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"860_CR16","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1016\/j.ejor.2008.07.015","volume":"197","author":"J Molina","year":"2009","unstructured":"Molina J, Santana LV, Hern\u00e1ndez-D\u00edaz AG, Coello CAC, Caballero R (2009) g-dominance: reference point based dominance for multiobjective metaheuristics. Eur J Oper Res 197(2):685\u2013692","journal-title":"Eur J Oper Res"},{"key":"860_CR17","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.swevo.2019.06.009","volume":"49","author":"F Wang","year":"2019","unstructured":"Wang F, Li Y, Zhang H, Hu T, Shen X (2019) An adaptive weight vector guided evolutionary algorithm for preference-based multi-objective optimization. Swarm Evol Comput 49:220\u2013233","journal-title":"Swarm Evol Comput"},{"issue":"3","key":"860_CR18","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1007\/s11269-020-02485-9","volume":"34","author":"R Tang","year":"2020","unstructured":"Tang R, Li K, Ding W, Wang Y, Zhou H, Fu G (2020) Reference point based multi-objective optimization of reservoir operation: a comparison of three algorithms. Water Resour Manag 34(3):1005\u20131020","journal-title":"Water Resour Manag"},{"key":"860_CR19","doi-asserted-by":"crossref","unstructured":"Deb K, Sundar J (2006) Reference point based multi-objective optimization using evolutionary algorithms. In: Proceedings of the Conference on Genetic and Evolutionary Computation, ACM, pp. 635\u2013642","DOI":"10.1145\/1143997.1144112"},{"issue":"5","key":"860_CR20","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1109\/TEVC.2018.2884133","volume":"23","author":"J Yi","year":"2018","unstructured":"Yi J, Bai J, He H, Peng J, Tang D (2018) ar-MOEA: a novel preference-based dominance relation for evolutionary multiobjective optimization. IEEE Trans Evolut Comput 23(5):788\u2013802","journal-title":"IEEE Trans Evolut Comput"},{"key":"860_CR21","doi-asserted-by":"crossref","unstructured":"Abraham A, Jain L (2005) Evolutionary multiobjective optimization. In: Evolutionary multiobjective optimization, Springer, pp. 1\u20136","DOI":"10.1007\/1-84628-137-7_1"},{"issue":"5","key":"860_CR22","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1109\/TFUZZ.2018.2880700","volume":"27","author":"K Li","year":"2018","unstructured":"Li K, Chen R, Savi\u0107 D, Yao X (2018) Interactive decomposition multiobjective optimization via progressively learned value functions. IEEE Trans Fuzzy Syst 27(5):849\u2013860","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"860_CR23","doi-asserted-by":"crossref","unstructured":"Gong M, Liu F, Zhang W, Jiao L, Zhang Q (2011) Interactive MOEA\/D for multi-objective decision making. In: Proceedings of the Conference on Genetic and Evolutionary Computation, ACM, pp. 721\u2013728","DOI":"10.1145\/2001576.2001675"},{"issue":"4","key":"860_CR24","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1109\/TEVC.2019.2951217","volume":"24","author":"Y-N Guo","year":"2019","unstructured":"Guo Y-N, Zhang X, Gong D-W, Zhang Z, Yang J-J (2019) Novel interactive preference-based multiobjective evolutionary optimization for bolt supporting networks. IEEE Trans Evolut Comput 24(4):750\u2013764","journal-title":"IEEE Trans Evolut Comput"},{"key":"860_CR25","doi-asserted-by":"crossref","unstructured":"Hakanen J, Chugh T, Sindhya K, Jin Y, Miettinen K (2016) Connections of reference vectors and different types of preference information in interactive multiobjective evolutionary algorithms. In: Proceedings of 2016 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 1\u20138","DOI":"10.1109\/SSCI.2016.7850220"},{"issue":"3","key":"860_CR26","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1109\/TEVC.2012.2196800","volume":"17","author":"A Ponsich","year":"2012","unstructured":"Ponsich A, Jaimes AL, Coello CAC (2012) A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications. IEEE Trans Evol Comput 17(3):321\u2013344","journal-title":"IEEE Trans Evol Comput"},{"key":"860_CR27","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.neucom.2020.12.022","volume":"430","author":"H Zhao","year":"2021","unstructured":"Zhao H, Chen Z-G, Zhan Z-H, Kwong S, Zhang J (2021) Multiple populations co-evolutionary particle swarm optimization for multi-objective cardinality constrained portfolio optimization problem. Neurocomputing 430:58\u201370","journal-title":"Neurocomputing"},{"issue":"1","key":"860_CR28","first-page":"71","volume":"7","author":"HM Markowits","year":"1952","unstructured":"Markowits HM (1952) Portfolio selection. J Finance 7(1):71\u201391","journal-title":"J Finance"},{"issue":"4","key":"860_CR29","first-page":"1","volume":"54","author":"B Afsar","year":"2021","unstructured":"Afsar B, Miettinen K, Ruiz F (2021) Assessing the performance of interactive multiobjective optimization methods: a survey. ACM Computi Surv (CSUR) 54(4):1\u201327","journal-title":"ACM Computi Surv (CSUR)"},{"key":"860_CR30","doi-asserted-by":"crossref","unstructured":"Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of Congress on Evolutionary Computation (CEC), Vol.\u00a03, IEEE, pp. 1875\u20131882","DOI":"10.1109\/CEC.1999.785502"},{"key":"860_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106192","volume":"90","author":"J Zou","year":"2020","unstructured":"Zou J, Yang Q, Yang S, Zheng J (2020) Ra-dominance: a new dominance relationship for preference-based evolutionary multiobjective optimization. Appl Soft Comput 90:106192","journal-title":"Appl Soft Comput"},{"key":"860_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2021.100866","volume":"63","author":"R Szlapczynski","year":"2021","unstructured":"Szlapczynski R, Szlapczynska J (2021) W-dominance: tradeoff-inspired dominance relation for preference-based evolutionary multi-objective optimization. Swarm Evolut Comput 63:100866","journal-title":"Swarm Evolut Comput"},{"key":"860_CR33","doi-asserted-by":"crossref","unstructured":"Luo W, Shi L, Lin X, Coello CAC (2019) The $$\\hat{g}$$-dominance relation for preference-based evolutionary multi-objective optimization. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), IEEE, pp. 2418\u20132425","DOI":"10.1109\/CEC.2019.8790321"},{"key":"860_CR34","doi-asserted-by":"crossref","unstructured":"Luo W, Lin X (2017) Recent advances in clonal selection algorithms and applications. In: Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 1\u20138","DOI":"10.1109\/SSCI.2017.8285340"},{"key":"860_CR35","unstructured":"Coello CAC, Cort\u00e9s NC (2022) An approach to solve multiobjective optimization problems based on an artificial immune system"},{"issue":"2","key":"860_CR36","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/s10710-005-6164-x","volume":"6","author":"CAC Coello","year":"2005","unstructured":"Coello CAC, Cort\u00e9s NC (2005) Solving multiobjective optimization problems using an artificial immune system. Gen Program Evol Mach 6(2):163\u2013190","journal-title":"Gen Program Evol Mach"},{"issue":"3","key":"860_CR37","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/TEVC.2002.1011539","volume":"6","author":"LN De Castro","year":"2002","unstructured":"De Castro LN, Von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6(3):239\u2013251","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"860_CR38","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1162\/evco.2008.16.2.225","volume":"16","author":"M Gong","year":"2008","unstructured":"Gong M, Jiao L, Du H, Bo L (2008) Multiobjective immune algorithm with nondominated neighbor-based selection. Evol Comput 16(2):225\u2013255","journal-title":"Evol Comput"},{"key":"860_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2018.10.010","volume":"50","author":"W Luo","year":"2019","unstructured":"Luo W, Lin X, Zhu T, Xu P (2019) A clonal selection algorithm for dynamic multimodal function optimization. Swarm Evol Comput 50:100459","journal-title":"Swarm Evol Comput"},{"key":"860_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112972","volume":"141","author":"A Got","year":"2020","unstructured":"Got A, Moussaoui A, Zouache D (2020) A guided population archive whale optimization algorithm for solving multiobjective optimization problems. Expert Syst Appl 141:112972","journal-title":"Expert Syst Appl"},{"issue":"8","key":"860_CR41","doi-asserted-by":"publisher","first-page":"2654","DOI":"10.1016\/j.asoc.2012.04.005","volume":"12","author":"Y Qi","year":"2012","unstructured":"Qi Y, Liu F, Liu M, Gong M, Jiao L (2012) Multi-objective immune algorithm with baldwinian learning. Appl Soft Comput 12(8):2654-2674","journal-title":"Appl Soft Comput"},{"key":"860_CR42","doi-asserted-by":"crossref","unstructured":"Deb K, Thiele L, Laumanns M, Zitzler E (2002) Scalable multi-objective optimization test problems. In: Proceedings of the Congress on Evolutionary Computation, Vol.\u00a01, IEEE pp. 825\u2013830","DOI":"10.1109\/CEC.2002.1007032"},{"issue":"5","key":"860_CR43","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1109\/TEVC.2005.861417","volume":"10","author":"S Huband","year":"2006","unstructured":"Huband S, Hingston P, Barone L, While L (2006) A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans Evo Comput 10(5):477\u2013506","journal-title":"IEEE Trans Evo Comput"},{"key":"860_CR44","unstructured":"Zitzler E, Laumanns M, Thiele L (2022) Spea2: improving the strength pareto evolutionary algorithm, TIK-report 103"},{"key":"860_CR45","doi-asserted-by":"crossref","unstructured":"Li L, Yen GG, Sahoo A, Chang L, Gu T (2021) On the estimation of pareto front and dimensional similarity in many-objective evolutionary algorithm. Inform Sci 563:375\u2013400","DOI":"10.1016\/j.ins.2021.03.008"},{"key":"860_CR46","unstructured":"Triguero I, Gonz\u00e1lez S, Moyano JM, Garc\u00eda\u00a0L\u00f3pez S, Alcal\u00e1\u00a0Fern\u00e1ndez J, Luengo\u00a0Mart\u00edn J, Fern\u00e1ndez\u00a0Hilario A, Jes\u00fas\u00a0D\u00edaz MJd, S\u00e1nchez L, Herrera\u00a0Triguero F, et\u00a0al. (2022) KEEL 3.0: an open source software for multi-stage analysis in data mining"},{"key":"860_CR47","doi-asserted-by":"crossref","unstructured":"Zhao P, Gao S, Yang N (2020) Solving multi-objective portfolio optimization problem based on MOEA\/D. In: Proceedings of International Conference on Advanced Computational Intelligence (ICACI), IEEE, pp. 30\u201337","DOI":"10.1109\/ICACI49185.2020.9177505"},{"issue":"4","key":"860_CR48","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1109\/TETCI.2018.2868939","volume":"3","author":"Y-H Chou","year":"2018","unstructured":"Chou Y-H, Kuo S-Y, Jiang Y-C (2018) A novel portfolio optimization model based on trend ratio and evolutionary computation. IEEE Trans Emerg Top Comput Intell 3(4):337\u2013350","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"key":"860_CR49","unstructured":"Najafabadi ZM, Bijari M, Khashei M (2022) Making investment decisions in stock markets using a forecasting-markowitz based decision-making approaches. J Model Manag"},{"issue":"13","key":"860_CR50","doi-asserted-by":"publisher","first-page":"1271","DOI":"10.1016\/S0305-0548(99)00074-X","volume":"27","author":"T-J Chang","year":"2000","unstructured":"Chang T-J, Meade N, Beasley JE, Sharaiha YM (2000) Heuristics for cardinality constrained portfolio optimisation. Comput Oper Res 27(13):1271\u20131302","journal-title":"Comput Oper Res"},{"key":"860_CR51","doi-asserted-by":"crossref","unstructured":"Chen Y, Zhou A (2019) MOEA\/D with an improved multi-dimensional mapping coding scheme for constrained multi-objective portfolio optimization. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), IEEE, pp. 1742\u20131749","DOI":"10.1109\/CEC.2019.8790165"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-022-00860-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-022-00860-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-022-00860-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T09:26:23Z","timestamp":1681809983000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-022-00860-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,12]]},"references-count":51,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["860"],"URL":"https:\/\/doi.org\/10.1007\/s40747-022-00860-0","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,12]]},"assertion":[{"value":"7 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}